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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy import*\n",
      "from scipy import*\n",
      "from pylab import*\n",
      "from matplotlib import cm\n",
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib.animation as animation\n",
      "import sys,os,time\n",
      "%cd -0\n",
      "wdir=os.getcwd()\n",
      "\n",
      "class ListTable(list):\n",
      "    \"\"\" Overridden list class which takes a 2-dimensional list of \n",
      "        the form [[1,2,3],[4,5,6]], and renders an HTML Table in \n",
      "        IPython Notebook. \"\"\"\n",
      "    \n",
      "    def _repr_html_(self):\n",
      "        html = [\"<table>\"]\n",
      "        for row in self:\n",
      "            html.append(\"<tr>\")\n",
      "            \n",
      "            for col in row:\n",
      "                html.append(\"<td>{0}</td>\".format(col))\n",
      "            \n",
      "            html.append(\"</tr>\")\n",
      "        html.append(\"</table>\")\n",
      "        return ''.join(html)\n",
      "\n",
      "\n",
      "def Math2Py(path):\n",
      "    ar=open(path)\n",
      "    ALis=ar.readlines()\n",
      "    ar.close()\n",
      "    NALis=[]\n",
      "    for linea in ALis:\n",
      "        NALis.append(map(eval,linea.replace(\"*^\",\"e\").replace(\"*I\",\"j\").split(\"\\t\")))\n",
      "    #NALis=map(eval,linea.replace(\"*^\",\"e\").replace(\"*I\",\"j\").split(\"\\t\"))\n",
      "    return array(NALis)\n",
      "        \n",
      "    \n",
      "def Mpeaks(V,offset=3):\n",
      "    Vu=V/V.max()\n",
      "    sx=array([[0,-1],[1,0]])\n",
      "    a=array([1,Vu[1]-Vu[0]])\n",
      "    n1=np.dot(sx,a)\n",
      "    ey=array([0,1])\n",
      "    mint=minimos=array([],dtype=int)\n",
      "    maxt=maximos=array([],dtype=int)\n",
      "    for i in range(1,len(Vu)-1):\n",
      "        b=array([1,Vu[i+1]-Vu[i]])\n",
      "        n2=np.dot(sx,b)\n",
      "        if (sign(cross(n1,ey))==sign(cross(n2,ey))):\n",
      "            n1=n2\n",
      "        else:            \n",
      "            if Vu[i]>=1E-2:\n",
      "                if sign(cross(n1,ey))<0:\n",
      "                    maximos=append(maximos,i)\n",
      "                else:\n",
      "                    minimos=append(minimos,i)\n",
      "        n1=n2  \n",
      "    ###################\n",
      "#    if len(maximos)==0:\n",
      "#        maximos=append(maximos,0)\n",
      "#    if len(minimos)==0:\n",
      "#        minimos=append(minimos,0)\n",
      "        \n",
      "    if offset>=len(maximos):\n",
      "        offsetma=len(maximos)\n",
      "    else:\n",
      "        offsetma=offset        \n",
      "    if offset>=len(minimos):\n",
      "        offsetmi=len(minimos)\n",
      "    else:\n",
      "        offsetmi=offset\n",
      "    #######################    \n",
      "    for i in range(offsetma):   \n",
      "        ma1=V[maximos].argmax()\n",
      "        maxt=append(maxt,maximos[ma1])\n",
      "        maximos=delete(maximos,ma1)\n",
      "###########\n",
      "    for i in range(offsetmi):   \n",
      "        mi1=V[minimos].argmin()\n",
      "        mint=append(mint,minimos[mi1])\n",
      "        minimos=delete(minimos,mi1)\n",
      "    return mint.tolist(),maxt.tolist()\n",
      "\n",
      "def CS(num):\n",
      "    if abs(num)<10:\n",
      "        if num<0:\n",
      "            txt=\"-0%d\"%int(abs(num))\n",
      "        else:\n",
      "            txt=\"0%d\"%int(num)\n",
      "    else:\n",
      "        txt=\"%d\"%int(num)\n",
      "    return txt\n",
      "\n",
      "def hms(tf):\n",
      "    hor=(int(tf/3600))  \n",
      "    minu=int((tf-(hor*3600))/60)  \n",
      "    seg=int(tf-((hor*3600)+(minu*60)))  \n",
      "    tfx=CS(hor)+\"h\"+CS(minu)+\"m\"+CS(seg)+\"s|\"\n",
      "    return tfx\n",
      "    \n",
      "\n",
      "\n",
      "\n",
      "os.chdir(\"Data\")\n",
      "directorios=filter(os.path.isdir, os.listdir(os.getcwd()))\n",
      "directorios.sort()\n",
      "#directorios=os.listdir(os.getcwd()+\"/Datos\")\n",
      "os.chdir(wdir)\n",
      "#list(enumerate(directorios))\n",
      "tabla=ListTable()\n",
      "tabla.append(['i','$g/\\hbar$','Nst','$\\hbar$','$EmC$','$EmX$','$gR$','$t_i$','$t_f$','$\\Delta t$','$x_i$','$x_f$','$\\Delta x$','$a_{0c}$','$a_{0x}$','$\\omega_{0c}$','$\\omega_{0x}$','$\\gamma_C$','$\\gamma_X$','nombre'])\n",
      "for i in range(len(directorios)):\n",
      "    os.chdir(\"Data/\"+directorios[i])\n",
      "    (g,Nst,hbar,EmC,EmX,gR,ti,tf,Dt,xi,xf,Dx,a0c,a0x,W0c,W0x,gammaC,gammaX)=18*('X',)\n",
      "    %run  constants.py\n",
      "    xi=x[0]\n",
      "    xf=x[-1]\n",
      "    ti=t[0]\n",
      "    tf=t[-1]\n",
      "    filaT=map(lambda x:x if str(type(x))==\"<type 'str'>\" else x if str(type(x))==\"<type 'int'>\" else round(x,3) ,\n",
      "              [i,g,Nst,hbar,EmC,EmX,gR,ti,tf,Dt,xi,xf,Dx,a0c,a0x,W0c,W0x,gammaC,gammaX,directorios[i]])\n",
      "    tabla.append(filaT)\n",
      "    #%cd -0\n",
      "    os.chdir(wdir)\n",
      "\n",
      "tabla"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/home/fabrice/Guirales/1D_Polariton\n"
       ]
      },
      {
       "html": [
        "<table><tr><td>i</td><td>$g/\\hbar$</td><td>Nst</td><td>$\\hbar$</td><td>$EmC$</td><td>$EmX$</td><td>$gR$</td><td>$t_i$</td><td>$t_f$</td><td>$\\Delta t$</td><td>$x_i$</td><td>$x_f$</td><td>$\\Delta x$</td><td>$a_{0c}$</td><td>$a_{0x}$</td><td>$\\omega_{0c}$</td><td>$\\omega_{0x}$</td><td>$\\gamma_C$</td><td>$\\gamma_X$</td><td>nombre</td></tr><tr><td>0</td><td>0.0</td><td>200</td><td>0.657</td><td>0.025</td><td>2</td><td>2</td><td>0.0</td><td>29.3</td><td>0.098</td><td>-70.0</td><td>70.0</td><td>0.274</td><td>26.292</td><td>0.329</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>Gauss1D2014_07_03_18_48_11</td></tr><tr><td>1</td><td>0.0</td><td>200</td><td>0.657</td><td>0.56</td><td>500</td><td>4.001</td><td>0.0</td><td>29.3</td><td>0.098</td><td>-70.0</td><td>70.0</td><td>0.274</td><td>1.173</td><td>0.001</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>Gauss1D2014_07_03_19_11_43</td></tr><tr><td>2</td><td>X</td><td>200</td><td>0.657</td><td>0.025</td><td>2</td><td>2</td><td>0.0</td><td>29.3</td><td>0.098</td><td>-500.0</td><td>500.0</td><td>0.244</td><td>26.292</td><td>0.329</td><td>0.0</td><td>0.0</td><td>0</td><td>0</td><td>Gauss1D_DavidFake_2014_07_03_18_18_26</td></tr></table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "[['i',\n",
        "  '$g/\\\\hbar$',\n",
        "  'Nst',\n",
        "  '$\\\\hbar$',\n",
        "  '$EmC$',\n",
        "  '$EmX$',\n",
        "  '$gR$',\n",
        "  '$t_i$',\n",
        "  '$t_f$',\n",
        "  '$\\\\Delta t$',\n",
        "  '$x_i$',\n",
        "  '$x_f$',\n",
        "  '$\\\\Delta x$',\n",
        "  '$a_{0c}$',\n",
        "  '$a_{0x}$',\n",
        "  '$\\\\omega_{0c}$',\n",
        "  '$\\\\omega_{0x}$',\n",
        "  '$\\\\gamma_C$',\n",
        "  '$\\\\gamma_X$',\n",
        "  'nombre'],\n",
        " [0,\n",
        "  0.0,\n",
        "  200,\n",
        "  0.657,\n",
        "  0.025,\n",
        "  2,\n",
        "  2,\n",
        "  0.0,\n",
        "  29.3,\n",
        "  0.098,\n",
        "  -70.0,\n",
        "  70.0,\n",
        "  0.274,\n",
        "  26.292,\n",
        "  0.329,\n",
        "  0.0,\n",
        "  0.0,\n",
        "  0,\n",
        "  0,\n",
        "  'Gauss1D2014_07_03_18_48_11'],\n",
        " [1,\n",
        "  0.0,\n",
        "  200,\n",
        "  0.657,\n",
        "  0.56,\n",
        "  500,\n",
        "  4.001,\n",
        "  0.0,\n",
        "  29.3,\n",
        "  0.098,\n",
        "  -70.0,\n",
        "  70.0,\n",
        "  0.274,\n",
        "  1.173,\n",
        "  0.001,\n",
        "  0.0,\n",
        "  0.0,\n",
        "  0,\n",
        "  0,\n",
        "  'Gauss1D2014_07_03_19_11_43'],\n",
        " [2,\n",
        "  'X',\n",
        "  200,\n",
        "  0.657,\n",
        "  0.025,\n",
        "  2,\n",
        "  2,\n",
        "  0.0,\n",
        "  29.3,\n",
        "  0.098,\n",
        "  -500.0,\n",
        "  500.0,\n",
        "  0.244,\n",
        "  26.292,\n",
        "  0.329,\n",
        "  0.0,\n",
        "  0.0,\n",
        "  0,\n",
        "  0,\n",
        "  'Gauss1D_DavidFake_2014_07_03_18_18_26']]"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i=1\n",
      "os.chdir(wdir)\n",
      "sufix=directorios[i]\n",
      "path=\"Data/\"+sufix\n",
      "os.chdir(path)\n",
      "(g,Nst,hbar,EmC,EmX,gR,Sig0,a0c,a0x,W0c,W0x,gammaC,gammaX,C0,wl,Sigl,DT1,t1)=18*('X',)\n",
      "#from constantes import *\n",
      "%run  constants.py\n",
      "#var=%system more constantes.py\n",
      "print [i,g,Nst,hbar,EmC,EmX,gR,Sig0,a0c,a0x,W0c,W0x,gammaC,gammaX,C0,wl,Sigl,DT1,t1,directorios[i]]\n",
      "\n",
      "x=load(os.getcwd()+\"/x\"+sufix+\".npy\")\n",
      "t=load(os.getcwd()+\"/t\"+sufix+\".npy\")\n",
      "Dx=x[1]-x[0]\n",
      "Dt=t[1]-t[0]\n",
      "Nt=len(t)\n",
      "Nx=len(x)\n",
      "#x2=load(sufix2+\"/x\"+sufix2)\n",
      "#t2=load(sufix2+\"/t\"+sufix2)\n",
      "PsiNc=load(os.getcwd()+\"/\"+prefix0+sufix+\".npy\")\n",
      "PsiNx=load(os.getcwd()+\"/\"+prefix1+sufix+\".npy\")\n",
      "os.chdir(wdir)\n",
      "\n",
      "Y1=PsiNc**2\n",
      "Y2=PsiNx**2\n",
      "#X,Y=meshgrid(x,x)\n",
      "\n",
      "\n",
      "I1=array([round(sum(abs(Y1[i]))*Dx,3) for i in range(Nt)])\n",
      "I2=array([round(sum(abs(Y2[i]))*Dx,3) for i in range(Nt)])\n",
      "IT=(abs(I1)+abs(I2));\n",
      "\n",
      "\n",
      "\n",
      "pl1=abs(Y1)\n",
      "pl2=abs(Y2)\n",
      "\n",
      "\n",
      "picosC=[]\n",
      "picosX=[]\n",
      "\n",
      "for fr in range(Nt):\n",
      "    mi,peakc=Mpeaks(pl1[fr],offset=5)\n",
      "    picosC.append(peakc)\n",
      "    mi,peakx=Mpeaks(pl2[fr],offset=5)\n",
      "    picosX.append(peakx)\n",
      "\n",
      "maxi=abs(pl1).max()\n",
      "    #maxi=max([list(pl1[i][picosC[i]]) for i in range(Nt)])[0]\n",
      "print \"Cargado\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1, 0.0, 200, 0.65731, 0.560337, 500, 4.001, 'X', 1.1730619252342787, 0.0013146199999999999, 0.0, 0.0, 0, 0, 'X', 'X', 'X', 'X', 'X', 'Gauss1D2014_07_03_19_11_43']\n",
        "Cargado"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(x,abs(Y2[2])**2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f5624508f50>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rd2LjN+uA/5nwut/qvAc4hrUS4/xU4y+xn2M18DLQPeE9P9UJ2Z1o68IAYCGw\nHvt9/In3emsn8brUEfs//qr33I91ngK8iP1ubsC62v1W54PYv/la4BmgM/6rsWiqaOni+ib2HwJa\nxlBKsbGXetxekuUy7B+o1Ht+uvfVb3UOwCYrbKElPPxW4+UJx3+EL4+b+aXOjl4NA7GaUo0butIb\nGOMtd8O6mEdgY5L3ea/fT8vP1rX/AfwemOc992OdlcAPveVO2IcaP9U5ENiMBQbAc8B0/FVjUc0F\nrveWbwD+j7ecPDvsdWxQ35Xnga+leN1vdb4AnMPx4eG3GhNdi3//zS/k+FmHyTMK/eQVYAotMybB\nAqY27RbF0x8bJ72MlpaH3+rsjv1hTuanOntiHxJ6YOH2KvZBLOcaw3JhxAeAXwEfYN0Z8cH5vhx/\ncmH8pENXhgCTsBlpVcD53ut+qvNq7/h/TnrdTzUm+yEtM/78Vme6k2j9ZiB2XbqlpD+J16V/Ae7F\nulLj/FbnWcDHwG+BVcBvgK74q85PaPlb+SGwF+sNybnGIN3PYwEt54Yk+hnWV/sT4A/Ad4H/jaVp\nKrGCVNeitTo7YYk/ERuXeR44O81+CllnazU+yPHjBK2dTuXqZ/lTWj59/gz4HOu7TafQdbbG5bGz\n1Q14CbgL+DTpvfh5AC5dCezCxjsq0qzjhzo7YbNNZ2Jjr/+LL7cyXdc5CLgb+7CwD+tluClpnaxq\nDFJ4pAsDsC6LKd7yi8Acb7kR67+P6++9Vkit1fkjbHAX7JfrGHAaxa8zXY3l2Ken6oQ6VmKDfn77\nWQLcgp1nNDnhNRd1tia5ngEc3zJyrRQLjqexbiuwT569gY+wk3hdXx/3Iux6eFcAJwAnY/X6rc4d\n3mO59/xF7MPYR/inzvOBxcAe7/nLWNeqn2osqlXApd7yZFr+8Vo76dCFGcBsb3ko1nQE/9UZl2rA\n3C81TsVmjJyW9Lrf6szmRFtXSrCLjP5L0uvpTuL1g0tpaXX6sc5F2P9tgFlYjX6q81xsZt2J2L9/\nJfBj/FVjUZ2P9dWuAZZw/D1F0p106EIp9olpLfaJviLhPT/VGbeZ46fq+qnGTcA2rCtjNXb5/jg/\n1QnpT5Z17RKs9buGlp/jVFo/ide1S2mZbeXHOs/FPrwmTiH3W5330TJVtxL7u+S3GkVERERERERE\nRERERERERERERERERERERERERKRQ/j812UlYCdKYHgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f56248a6610>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "fig2, axes = subplots(3,1)\n",
      "axes[0].plot(t,abs(I1),\"r\")\n",
      "#axes[0].set_xlim(0,2)\n",
      "axes[1].plot(t,abs(I2),\"b\")\n",
      "#axes[1].set_xlim(0,1.8)\n",
      "\n",
      "axes[2].plot(t,abs(I1),\"r\")\n",
      "axes[2].plot(t,abs(I2),\"b\")\n",
      "axes[2].plot(t,abs(IT),\"g\");\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VIpXaMSUPl+KXCNkWC2PSGVm42gFmf/vS5EfdrMwWi82HLZZ0RkJjY7O3PTBh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hUweAWWAweaFgTwYY6Ly20sn9kMMmVMkUEpqz2uhZD3A94PIMdFaOkM2MzGsUKarHoKQU1o2Y8I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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f56245390d0>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "-60.0"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "#def soliton(x,t):\n",
      "#    asol=0.15\n",
      "#    vsol=0.0\n",
      "#    x0sol=0.0\n",
      "#    return asol*exp(1.0j*(vsol*x+(asol**2-vsol**2)*t/2))/cosh(asol*(x-vsol*t-x0sol))\n",
      "\n",
      "%matplotlib inline\n",
      "#Y10=PsiNc[::,Nx/2]\n",
      "#Y20=PsiNx[::,Nx/2]\n",
      "\n",
      "maxi=abs(pl1).max()\n",
      "\n",
      "fig2, axes = plt.subplots(1,2)\n",
      "axes[0].plot(x,pl1[0])\n",
      "axes[1].plot(x,pl2[0])\n",
      "\n",
      "nl=int(Nt/10)\n",
      "\n",
      "print \"[__h__m__s|\"+(10*\"##\")+\"]\"\n",
      "sys.stdout.write(\"[\")\n",
      "start = time.time()\n",
      "\n",
      "def data_gen2(fr):\n",
      "    \n",
      "    peakc=picosC[fr]\n",
      "    peakx=picosX[fr]\n",
      "    if fr==1:\n",
      "        end=time.time()\n",
      "        tfi=(end-start)*Nt\n",
      "        sys.stdout.write(hms(tfi))\n",
      "    \n",
      "    lt=\"%d/%d\"%(fr,Nt)\n",
      "    axes[0].clear()\n",
      "    axes[1].clear()\n",
      "    \n",
      "    axes[0].set_title(\"Cavidad \"+lt)\n",
      "    axes[0].set_ylim(0,maxi)\n",
      "    axes[0].plot(x,pl1[fr])\n",
      "    if len(peakc)!=0:\n",
      "        for i in peakc:\n",
      "            axes[0].plot([x[i],x[i]],[0,pl1[fr][i]],marker='o') \n",
      "        \n",
      "    \n",
      "    axes[1].set_title(\"exciton \"+lt) \n",
      "    axes[1].set_ylim(0,maxi)\n",
      "    axes[1].plot(x,pl2[fr])\n",
      "#    axes[1].plot(x,abs(soliton(x,Dt*fr))**2)\n",
      "    if len(peakx)!=0:\n",
      "        for i in peakx:\n",
      "            axes[1].plot([x[i],x[i]],[0,pl2[fr][i]],marker='o')\n",
      "        \n",
      "    \n",
      "    \n",
      "    if (fr%nl==0)&(fr!=0):\n",
      "        sys.stdout.write(\"##\")\n",
      "\n",
      "#    axes[1].set_ylim(0,maxi)\n",
      "#picosC=array([],dtype=int)\n",
      "ani = animation.FuncAnimation(fig2, data_gen2, interval=100,frames=Nt, blit=False)\n",
      "\n",
      "ani.save('temporal1D.mp4',writer=\"avconv\", codec=\"libx264\")\n",
      "sys.stdout.write(\"]\")\n",
      "plt.close(fig2)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[__h__m__s|####################]\n",
        "[00h00m33s|"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "####"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "####"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "####"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "####"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "####"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "]"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "\n",
      "video = open(\"temporal1D.mp4\", \"rb\").read()\n",
      "video_encoded = video.encode(\"base64\")\n",
      "video_tag = '<video controls alt=\"test\" src=\"data:video/x-m4v;base64,{0}\">'.format(video_encoded)\n",
      "## width=\"600\" height=\"400\" \n",
      "HTML(video_tag)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "ABtkYXRhAAAAAQAAAABMYXZmNTQuMjAuNA==\n",
        "\">"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "<IPython.core.display.HTML at 0x7fd64225cc10>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "fig2, axes = plt.subplots(1,2)\n",
      "axes[0].set_title(\"Cavidad \")\n",
      "axes[0].imshow(pl1[::1,::]**1,extent=[x[0],x[-1],t[0],t[-1]],aspect='auto',origin='lower')\n",
      "#axes[0].set_interpolation('bilinear')\n",
      "axes[1].set_title(\"Exciton \")\n",
      "axes[1].imshow(pl2[::1,::]**1,extent=[x[0],x[-1],t[0],t[-1]],aspect='auto',origin='lower');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7fe68973fb10>"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Pruebas(test)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PsiCD=Math2Py(\"DSLPWF/Datas_solution_lowerpolariton_PsiC.dat\")\n",
      "PsiXD=Math2Py(\"DSLPWF/Datas_solution_lowerpolariton_PsiX.dat\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "I1D=array([round(sum(abs(PsiCD[i])**2)*Dx,3) for i in range(Nt)])\n",
      "I2D=array([round(sum(abs(PsiXD[i])**2)*Dx,3) for i in range(Nt)])\n",
      "ITD=IT=(abs(I1D)+abs(I2D))\n",
      "%matplotlib inline\n",
      "fig2, axes = subplots(3,1)\n",
      "axes[0].plot(abs(I1D),\"r\")\n",
      "#axes[0].set_xlim(0,1.53)\n",
      "axes[1].plot(abs(I2D),\"b\")\n",
      "#axes[1].set_xlim(0,1.8)\n",
      "\n",
      "axes[2].plot(abs(I1D),\"r\")\n",
      "axes[2].plot(abs(I2D),\"b\")\n",
      "axes[2].plot(abs(ITD),\"g\");\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pQASvNQLYTgPuBdYAtxJ5FSRJkiRpct1mbquSR4C7id7RHenYdsCNwIPADcC2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\nSZIkSZIkSZIkSZIkSZIkSZIkSVLd/H85RkGb5OyoZgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f5624e1c750>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "i=0\n",
      "plot(abs(PsiCD[i])**2)\n",
      "a=400\n",
      "xlim(len(x)/2-a,len(x)/2+a)\n",
      "print len(x[len(x)/2-a:len(x)/2+a])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "800\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ieUfrYLCrHPIEezewhRDuS4G1wJJUn1XALcBi4B7godg+CPwG8FZgBfCxBudKbWWwq2ry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\nSZIkSZIkSZIkSZIkSZIkSVI+/x9w/FXuC5oP5AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f562373d5d0>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(t)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 115,
       "text": [
        "300"
       ]
      }
     ],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def polasoliton(x):\n",
      "    def po1(k):\n",
      "        return (2.6556298300679924*(1.29818725*k**2 -sqrt(0.16000000000000003 + 1.6852901360625623*k**4))*\n",
      "                exp(-2.*k**2)/sqrt(1 + 6.25*abs(1.29818725*k**2 - sqrt(0.16000000000000003 + 1.6852901360625623*k**4))**2))\n",
      "    def po2(k):\n",
      "        return (1.062251932027197*exp(-2.*k**2)/sqrt(1 + 6.25*abs(1.29818725*k**2 -\n",
      "                sqrt(0.16000000000000003 + 1.6852901360625623*k**4))**2))\n",
      "        \n",
      "    Nxp = sqrt(2*pi*len(x))/Dx\n",
      "    Dkp = 2*pi/(Dx*Nxp)\n",
      "    Kf = pi/Dkp\n",
      "    Ki=arange(-Kf,Kf,Dkp)\n",
      "    xk=linspace(x[0],x[-1],Nxp)\n",
      "    Dxk=xk[1]-xk[0]\n",
      "    #linspace(-Kf,Kf,Nx)\n",
      "    ps1=array([po1(k) for k in Ki])\n",
      "    ps2=array([po2(k) for k in Ki])\n",
      "    Psi1=ifft(array([po1(k) for k in Ki]))*sqrt(len(x))\n",
      "    Psi2=ifft(array([po2(k) for k in Ki]))*sqrt(len(x))\n",
      "    return Psi1,Psi2,xk,Dxk\n",
      "\n",
      "PsiN1,PsiN2,xk,Dxk=polasoliton(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print '(',x[0],',',x[-1],')---(',xk[0],',',xk[-1],')'\n",
      "print len(x),'--',len(xk)\n",
      "print Dx,'--',Dxk\n",
      "print len(PsiCD[0]),'--',len(PsiN1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "( -500.0 , 500.0 )---( -500.0 , 500.0 )\n",
        "4096 -- 656\n",
        "0.2442002442 -- 1.52671755725\n",
        "4096 -- 4096\n"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ifpo1=ifft(po1)*sqrt(len(x))\n",
      "ifpo1[2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 104,
       "text": [
        "(-0.23921532096701331+1.6532585464132106e-13j)"
       ]
      }
     ],
     "prompt_number": 104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "fig2, axes = subplots(1,2)\n",
      "i=2\n",
      "print i\n",
      "axes[0].plot(abs(PsiCD[i][len(x)/2-a:len(x)/2+a])**2,'r')\n",
      "axes[0].plot(abs(Y1[i]),'b')\n",
      "#xlim(1000,3000);\n",
      "axes[1].plot(abs(PsiXD[i][len(x)/2-a:len(x)/2+a])**2,'r')\n",
      "axes[1].plot(abs(Y2[i]),'b')\n",
      "\n",
      "print abs(PsiCD[i]**2).max(), abs(PsiXD[i]**2).max()\n",
      "print abs(Y1[i]).max(), abs(Y2[i]).max()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2\n",
        "0.0726956740234 0.193158549461\n",
        "0.0726946104141 0.193137867047\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WbGa/3D5bknWZ7Nq1a365VqtRq9WyHlp6c2frjLQJ7q7vUmWW2dMxY68aG0DL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\niIiIiIiIiIiIiIiIiIiIiIiISL/8f3F7Up/82sK7AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f5623a1ec10>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "plot(abs(PsiXD[i])**2)\n",
      "plot(fftshift(abs(PsiN2)**2))\n",
      "#xlim(1000,3000);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BgjFb739msSMCqZtBoGAMPEfgqiFpAYNAwegMgmYTSiVHBFI/BoGCMUgQOEcgdTMIFIzO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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe689a96a50>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sufix"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "'Gauss1D_David_2014_07_01_19_08_54'"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "save(path+\"/PsiCD.npy\",PsiCD[0])\n",
      "save(path+\"/PsiXD.npy\",PsiXD[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "DK=2*pi/(Dx*Nx)\n",
      "kf = pi/Dx\n",
      "kl=arange(-kf,kf,DK)\n",
      "\n",
      "DE=hbar*2*pi/(Dt*Nt)\n",
      "Ef=hbar*pi/Dt\n",
      "En=arange(-Ef,Ef,DE)\n",
      "\n",
      "\n",
      "Dt1=t[1]-t[0]\n",
      "Nt1=len(t)\n",
      "DE1=hbar*2*pi/(Dt1*Nt1)\n",
      "E1f=hbar*pi/Dt1\n",
      "En1=arange(-E1f,E1f,DE1)\n",
      "\n",
      "#AFT1=abs(flipud(fftshift(fft2(PsiNc))))\n",
      "#AFT2=abs(flipud(fftshift(fft2(PsiNx))))\n",
      "AFT1=abs(fftshift(fft2(PsiNc)))\n",
      "AFT2=abs(fftshift(fft2(PsiNx)))\n",
      "   \n",
      "\n",
      "def egv(eg,k):\n",
      "    return (((EmC+EmX)*hbar**2*k**2+eg*sqrt(16.0*EmC**2*EmX**2*gR**2*hbar**2+ \n",
      "    (EmC-EmX)**2*hbar**4*k**4))/(4.0*EmC*EmX))\n",
      "\n",
      "fig2, axes = plt.subplots(1,2)\n",
      "axes[0].set_title(\"Cavity \")\n",
      "axes[0].imshow(abs(AFT1),extent=[kl[0],kl[-1],En[0],En[-1]],aspect='auto',origin='lower')\n",
      "axes[0].plot(kl,egv(1,kl),\"black\")\n",
      "axes[0].plot(kl,egv(-1,kl),\"black\")\n",
      "#axes[0].set_ylim(-3,3)\n",
      "#axes[0].set_interpolation('bilinear')\n",
      "axes[1].set_title(\"Exciton \")\n",
      "axes[1].imshow(abs(AFT2),extent=[kl[0],kl[-1],En[0],En[-1]],aspect='auto',origin='lower')\n",
      "axes[1].plot(kl,egv(1,kl),\"black\")\n",
      "axes[1].plot(kl,egv(-1,kl),\"black\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CWJ9ld6DfOZ5nHydhZ9rOuJ1tuzNh59m+tS/bGJ7ttLDbjqcNSAP+QM6qIMf2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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f55060c7d10>"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Esfera de Bloch"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def pc_i(i):\n",
      "    try: \n",
      "        sal=picosC[i][0] \n",
      "    except:\n",
      "        sal=0\n",
      "    return sal\n",
      "def px_i(i):\n",
      "    try: \n",
      "        sal=picosX[i][0] \n",
      "    except:\n",
      "        sal=0\n",
      "    return sal\n",
      "    \n",
      "pc=[pc_i(i)  for i in range(len(picosC))]\n",
      "px=[px_i(i)  for i in range(len(picosX))]\n",
      "\n",
      "Yc=pl1\n",
      "Yx=pl2\n",
      "\n",
      "alpha_n=array([Yc[i,pc[i]]/sqrt(abs(Yc[i,pc[i]])**2+abs(Yx[i,px[i]])**2) if sqrt(abs(Yc[i,pc[i]])**2+abs(Yx[i,px[i]])**2)!=0.0 else 0.0 for i in range(len(pl1))]);\n",
      "\n",
      "beta_n=array([Yx[i,px[i]]/sqrt(abs(Yc[i,pc[i]])**2+abs(Yx[i,px[i]])**2) if sqrt(abs(Yc[i,pc[i]])**2+abs(Yx[i,px[i]])**2)!=0.0 else 0.0 for i in range(len(pl1))]);\n",
      "\n",
      "theta_n=2*arccos(abs(alpha_n))\n",
      "\n",
      "phi_n=array([-1j*log(beta_n[i]*abs(alpha_n[i])/(alpha_n[i]*sin(theta_n[i]/2))) if alpha_n[i]!=0.0 and theta_n[i]!=0  else 0.0 for i in range(len(pl1))])\n",
      "\n",
      "Vec=array([sin(theta_n)*cos(phi_n),sin(theta_n)*sin(phi_n),cos(theta_n)])\n",
      "\n",
      "PR=real(transpose(Vec)) if round(abs(sum(imag(transpose(Vec)))),7) ==0 else 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#%matplotlib inline\n",
      "%matplotlib tk\n",
      "import matplotlib as mpl\n",
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "mpl.rcParams['legend.fontsize'] = 10\n",
      "\n",
      "fig3 = plt.figure()\n",
      "ax = fig3.gca(projection='3d')\n",
      "\n",
      "ue = np.linspace(0, 2 * np.pi, 20)\n",
      "ve = np.linspace(0, np.pi, 20)\n",
      "xe = outer(np.cos(ue), np.sin(ve))\n",
      "ye = outer(np.sin(ue), np.sin(ve))\n",
      "ze = outer(np.ones(np.size(ue)), np.cos(ve))\n",
      "plot_args2 = {'rstride': 1, 'cstride': 1,'cmap':cm.coolwarm,  'linewidth': 0.0, 'antialiased': True, 'alpha':0.3}\n",
      "ax.plot_surface(xe, ye, ze,  **plot_args2)\n",
      "\n",
      "x = PR[::,0]\n",
      "y = PR[::,1]\n",
      "z =PR[::,2]\n",
      "ax.set_xlim(-1,1)\n",
      "ax.set_ylim(-1,1)\n",
      "ax.set_zlim(-1,1)\n",
      "ax.scatter(x, y, z,c='g');\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib as mpl\n",
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "fig3 = plt.figure()\n",
      "ax = fig3.gca(projection='3d')\n",
      "\n",
      "#ax.scatter(x[0],y[0],z[0])\n",
      "\n",
      "nl=int(Nt/10)\n",
      "\n",
      "print \"[__h__m__s|\"+(10*\"##\")+\"]\"\n",
      "sys.stdout.write(\"[\")\n",
      "start = time.time()\n",
      "\n",
      "ue = np.linspace(0, 2 * np.pi, 20)\n",
      "ve = np.linspace(0, np.pi, 20)\n",
      "xe = outer(np.cos(ue), np.sin(ve))\n",
      "ye = outer(np.sin(ue), np.sin(ve))\n",
      "ze = outer(np.ones(np.size(ue)), np.cos(ve))\n",
      "plot_args2 = {'rstride': 1, 'cstride': 1,'cmap':cm.coolwarm,  'linewidth': 0.0, 'antialiased': True, 'alpha':0.3}\n",
      "ax.plot_surface(xe, ye, ze,  **plot_args2)\n",
      "\n",
      "\n",
      "\n",
      "def data_gen3(fr):\n",
      "\n",
      "    if fr==1:\n",
      "        end=time.time()\n",
      "        tfi=(end-start)*Nt\n",
      "        sys.stdout.write(hms(tfi))\n",
      "    \n",
      "    lt=\"%d/%d\"%(fr,Nt)\n",
      "#    ax.clear()\n",
      "    \n",
      " #   ax.set_title(\"Cavidad \"+lt)\n",
      "    ax.set_xlim(-1,1)\n",
      "    ax.set_ylim(-1,1)\n",
      "    ax.set_zlim(-1,1)\n",
      "    ax.scatter(x[fr],y[fr],z[fr],c='g')\n",
      "    \n",
      "    if (fr%nl==0)&(fr!=0):\n",
      "        sys.stdout.write(\"##\")\n",
      "\n",
      "#    axes[1].set_ylim(0,maxi)\n",
      "\n",
      "ani = animation.FuncAnimation(fig3, data_gen3, interval=200,frames=Nt, blit=False)\n",
      "\n",
      "ani.save('temporalBloch.mp4',writer=\"avconv\", codec=\"libx264\")\n",
      "sys.stdout.write(\"]\")\n",
      "plt.close(fig3)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[__h__m__s|####################]\n",
        "[00h00m15s|"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "####"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "####"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "####"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "####"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "##]"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "\n",
      "video = open(\"temporalBloch.mp4\", \"rb\").read()\n",
      "video_encoded = video.encode(\"base64\")\n",
      "video_tag = '<video controls alt=\"test\" src=\"data:video/x-m4v;base64,{0}\">'.format(video_encoded)\n",
      "## width=\"600\" height=\"400\" \n",
      "HTML(video_tag)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<IPython.core.display.HTML at 0x7fc898526490>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}